ArticleScientific reports2026
Oxide-informed explainable machine learning with uncertainty quantification for compressive strength prediction of SCM-blended pervious concrete.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
The compressive strength of pervious concrete is challenging to predict due to complex, non-linear interactions between chemical, mix design, and curing factors. This study presents an integrated data-driven framework combining oxide-level chemical descriptors, advanced ensemble machine learning, metaheuristic optimisation, explainable artificial intelligence, and uncertainty quantification for compressive strength prediction. A dataset of 659 samples from 20 published studies was assembled, incorporating CaO, SiO₂, Al₂O₃, Fe₂O₃, MgO, alkali content, aggregate properties, water content, and curing duration as inputs. Five models were developed and evaluated: Random Forest, AdaBoost, LightGBM, CatBoost, and a hybrid CatBoost-MOWCA. CatBoost achieved the best generalisation performance (R
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